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This article will guide you on what Open Source is and how you can start contributing to open source projects.

What is Open Source?

Open Source refers to something people can modify and share because its design is publicly accessible — Open Source

How to Get Started

Requirements; A PC, Internet Connection, Git (if you don’t have it installed, you can read this article on how to do so), The project that you want to contribute to, and a Github Account.

In this article, the GitHub Uniform Resource Locator (URL) for the project is and the URL for the forked repository is, …

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How to install Tensorflow-GPU on Windows 10

Requirements; Anaconda, A PC with Windows 10 OS, Internet connection and at least 1GB of data.

1. Run Anaconda

If you do not have Anaconda installed, you can download it from here and install then search for Anaconda in the Windows search bar. Run the Navigator and Anaconda prompt.

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2. Create an Environment

You can create a new environment by typing the following command on Anaconda Prompt.

conda create -n gputensorflow python=3.7

For the purpose of this article, the environment name is gputensorflow and we’re installing python version 3.7, you can choose any environment name.

3. Activate the Environment

conda activate gputensorflow

4. Install the ipykernel

To install the kernel, enter the following…

Scraping HTML Tables

Have you ever wondered how you can get data off a website that does not have an Application Programming Interface (API)? Well, continue reading this article and you will learn about how you can do that.

This article will cover the following sub topics;

  • Definition of Web Scraping
  • Web Scraping Workflow
  • Web Scraping Requirements
  • How to get the best website URLs for Web Scraping
  • Hands-On experience

Definition of Web Scraping

Web Scraping is the process of extracting data from a website.

Copying and Pasting information from a website can also be termed as web scraping but on a manual level. …

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What I learnt in the last month of the Program

The last month of the She Code Africa Mentoring Program was the most thrilling because I learnt new things while building my final project but my laptop had to be formatted due to some issues. Despite everything, I’m happy that I was able to learn and also submit my project.

This article is a summary of what I learnt in the last month of the program.

Learning Path:

  • Machine Learning
  • Software Engineering
  • Data Science Project

Machine Learning

Machine learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.

Machine learning algorithms are classified as supervised or unsupervised. I learnt two machine learning algorithms; K-Nearest Neighbors and Linear Regression algorithm. …

Building a Linear Regression Machine Learning model and Deploying it using Flask and then to Heroku

Building a Linear Regression Machine Learning Model

Here is a step by step process on how to do this;

  • Import libraries
  • Data Collection
  • Exploratory Data Analysis
  • Data Cleaning
  • Build model
  • Evaluate metrics

Import Libraries

I used Jupyter Notebook as the Integrated Development Environment (IDE). The libraries required are; numpy, pandas, matplotlib, pickle or joblib and scikit-learn. These are pre-installed in the latest version of Anaconda. If you don’t have any of these libraries you can pip install them or update conda.

Data Collection

The dataset used for this model is the Pima Indians Diabetes dataset which consists of several medical predictor variables and one target variable, Outcome. Predictor variables include the number of pregnancies the patient has had, their Body Mass Index, insulin level, glucose level, diabetes pedigree function, blood pressure, skin thickness and age. …

Image from — A lady sitting on a desk using a laptop
Image from — A lady sitting on a desk using a laptop

What I learnt in the Second Month of the Program

To understand this article better, I would advice that you read the piece above if you have not already done that.

This article is a summary of what I learnt in the second month of the program.

Learning Path:

· Python Libraries for Data Analysis

· Data Wrangling

· Mathematics for Data Science

· Analysis of a Dataset

Python Libraries for Data Analysis

I learnt the rudiments of NumPy and Pandas libraries for data analysis.

NumPy stands for Numerical Python. It is a library consisting of multidimensional array objects and a collection of routines for processing these arrays. …

Basic Analysis of Google Play Store Apps dataset

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This analysis was performed on Google Play Store App dataset from Kaggle using Python . In this dataset, there are 10,842 applications and each application has the following details: Application name, Category, Rating (on a scale of 5), Number of Reviews, Size of the app, Number of installs, Application Type — Free or Paid, Price, Content Rating, Genres, Last updated On, Current Version, Required Android Version.

In order to get 1,000+ downloads on your Android Application, you need to know the current statistics of the android market.

This article will display the results of the following questions;

1. What category of applications have the highest number of installs? …

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Last month was quite exciting for me as an aspiring Data Scientist; I was selected to participate as a Mentee in the 1st Cohort of the She Code Africa Mentoring Program, Data Science track. I was assigned a Mentor and a Learning Path. Needless to say, I was filled with overwhelming joy to advance my Data Science journey with an experienced mind in the field solidly behind my endeavor.

This article is a summary of what I’ve learnt so far.

Learning Path:

· Basic SQL.

· Tableau for Data Visualization.

· Python

· Git

Each path had its assignments and resources.

Basic SQL

I learnt the fundamentals of SQL Joins, SQL Queries and SQL Aggregate functions. SQL Joins are used to combine rows or columns from two or more tables, SQL Aggregate functions are used to produce summarized data from a database, these functions are; COUNT, MAX, MIN, SUM, AVG…


Ekemini Okpongkpong

Aspiring Data Scientist | Tech Savvy

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